Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations558837
Missing cells123494
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory371.7 MiB
Average record size in memory697.4 B

Variable types

Numeric5
Text8
Categorical3

Alerts

color is highly overall correlated with transmissionHigh correlation
mmr is highly overall correlated with odometer and 2 other fieldsHigh correlation
odometer is highly overall correlated with mmr and 2 other fieldsHigh correlation
sellingprice is highly overall correlated with mmr and 2 other fieldsHigh correlation
transmission is highly overall correlated with colorHigh correlation
year is highly overall correlated with mmr and 2 other fieldsHigh correlation
transmission is highly imbalanced (90.1%)Imbalance
interior is highly imbalanced (50.4%)Imbalance
make has 10301 (1.8%) missing valuesMissing
model has 10399 (1.9%) missing valuesMissing
trim has 10715 (1.9%) missing valuesMissing
body has 13195 (2.4%) missing valuesMissing
transmission has 65321 (11.7%) missing valuesMissing
condition has 11820 (2.1%) missing valuesMissing

Reproduction

Analysis started2025-09-29 15:59:38.469931
Analysis finished2025-09-29 16:00:20.765257
Duration42.3 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0389
Minimum1982
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-09-29T21:00:20.849282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile2002
Q12007
median2012
Q32013
95-th percentile2014
Maximum2015
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9668636
Coefficient of variation (CV)0.0019735258
Kurtosis1.0105063
Mean2010.0389
Median Absolute Deviation (MAD)2
Skewness-1.1832259
Sum1.1232841 × 109
Variance15.736007
MonotonicityNot monotonic
2025-09-29T21:00:20.988192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2012102315
18.3%
201398168
17.6%
201481070
14.5%
201148548
8.7%
200831502
 
5.6%
200730845
 
5.5%
200626913
 
4.8%
201026485
 
4.7%
200521394
 
3.8%
200920594
 
3.7%
Other values (24)71003
12.7%
ValueCountFrequency (%)
19822
 
< 0.1%
19831
 
< 0.1%
19845
 
< 0.1%
198510
 
< 0.1%
198611
 
< 0.1%
19878
 
< 0.1%
198811
 
< 0.1%
198920
 
< 0.1%
199049
< 0.1%
199167
< 0.1%
ValueCountFrequency (%)
20159437
 
1.7%
201481070
14.5%
201398168
17.6%
2012102315
18.3%
201148548
8.7%
201026485
 
4.7%
200920594
 
3.7%
200831502
 
5.6%
200730845
 
5.5%
200626913
 
4.8%

make
Text

Missing 

Distinct96
Distinct (%)< 0.1%
Missing10301
Missing (%)1.8%
Memory size29.1 MiB
2025-09-29T21:00:21.249604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.9952236
Min length2

Characters and Unicode

Total characters3288596
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowKia
2nd rowKia
3rd rowBMW
4th rowVolvo
5th rowBMW
ValueCountFrequency (%)
ford94001
17.1%
chevrolet60587
 
11.0%
nissan54017
 
9.8%
toyota39966
 
7.3%
dodge30956
 
5.6%
honda27351
 
5.0%
hyundai21837
 
4.0%
bmw20793
 
3.8%
kia18084
 
3.3%
chrysler17485
 
3.2%
Other values (54)165367
30.0%
2025-09-29T21:00:21.607810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o328819
 
10.0%
e300106
 
9.1%
a235580
 
7.2%
r230200
 
7.0%
d215895
 
6.6%
n186317
 
5.7%
i184908
 
5.6%
s178494
 
5.4%
t128322
 
3.9%
l116781
 
3.6%
Other values (39)1183174
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3288596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o328819
 
10.0%
e300106
 
9.1%
a235580
 
7.2%
r230200
 
7.0%
d215895
 
6.6%
n186317
 
5.7%
i184908
 
5.6%
s178494
 
5.4%
t128322
 
3.9%
l116781
 
3.6%
Other values (39)1183174
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3288596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o328819
 
10.0%
e300106
 
9.1%
a235580
 
7.2%
r230200
 
7.0%
d215895
 
6.6%
n186317
 
5.7%
i184908
 
5.6%
s178494
 
5.4%
t128322
 
3.9%
l116781
 
3.6%
Other values (39)1183174
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3288596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o328819
 
10.0%
e300106
 
9.1%
a235580
 
7.2%
r230200
 
7.0%
d215895
 
6.6%
n186317
 
5.7%
i184908
 
5.6%
s178494
 
5.4%
t128322
 
3.9%
l116781
 
3.6%
Other values (39)1183174
36.0%

model
Text

Missing 

Distinct973
Distinct (%)0.2%
Missing10399
Missing (%)1.9%
Memory size29.5 MiB
2025-09-29T21:00:22.038063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length23
Mean length6.7691243
Min length1

Characters and Unicode

Total characters3712445
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)< 0.1%

Sample

1st rowSorento
2nd rowSorento
3rd row3 Series
4th rowS60
5th row6 Series Gran Coupe
ValueCountFrequency (%)
altima19432
 
2.9%
series15429
 
2.3%
grand14928
 
2.2%
f-15014527
 
2.2%
150014476
 
2.2%
fusion13639
 
2.0%
camry13515
 
2.0%
escape12027
 
1.8%
focus10463
 
1.6%
g9333
 
1.4%
Other values (740)531345
79.4%
2025-09-29T21:00:22.713483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a376585
 
10.1%
r277169
 
7.5%
e269750
 
7.3%
o195221
 
5.3%
n184979
 
5.0%
i170339
 
4.6%
s149945
 
4.0%
t136207
 
3.7%
l132705
 
3.6%
C123260
 
3.3%
Other values (56)1696285
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3712445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a376585
 
10.1%
r277169
 
7.5%
e269750
 
7.3%
o195221
 
5.3%
n184979
 
5.0%
i170339
 
4.6%
s149945
 
4.0%
t136207
 
3.7%
l132705
 
3.6%
C123260
 
3.3%
Other values (56)1696285
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3712445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a376585
 
10.1%
r277169
 
7.5%
e269750
 
7.3%
o195221
 
5.3%
n184979
 
5.0%
i170339
 
4.6%
s149945
 
4.0%
t136207
 
3.7%
l132705
 
3.6%
C123260
 
3.3%
Other values (56)1696285
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3712445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a376585
 
10.1%
r277169
 
7.5%
e269750
 
7.3%
o195221
 
5.3%
n184979
 
5.0%
i170339
 
4.6%
s149945
 
4.0%
t136207
 
3.7%
l132705
 
3.6%
C123260
 
3.3%
Other values (56)1696285
45.7%

trim
Text

Missing 

Distinct1963
Distinct (%)0.4%
Missing10715
Missing (%)1.9%
Memory size28.4 MiB
2025-09-29T21:00:23.131649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length37
Mean length4.7365805
Min length1

Characters and Unicode

Total characters2596224
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)< 0.1%

Sample

1st rowLX
2nd rowLX
3rd row328i SULEV
4th rowT5
5th row650i
ValueCountFrequency (%)
base56122
 
8.3%
se48390
 
7.2%
s30312
 
4.5%
lx21376
 
3.2%
limited20582
 
3.1%
lt20224
 
3.0%
2.518864
 
2.8%
xlt18796
 
2.8%
ls17932
 
2.7%
sport17602
 
2.6%
Other values (963)402154
59.8%
2025-09-29T21:00:23.841293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L214993
 
8.3%
S206476
 
8.0%
e155206
 
6.0%
i135234
 
5.2%
E127024
 
4.9%
124233
 
4.8%
T120883
 
4.7%
a108838
 
4.2%
r97787
 
3.8%
X91515
 
3.5%
Other values (62)1214035
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2596224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L214993
 
8.3%
S206476
 
8.0%
e155206
 
6.0%
i135234
 
5.2%
E127024
 
4.9%
124233
 
4.8%
T120883
 
4.7%
a108838
 
4.2%
r97787
 
3.8%
X91515
 
3.5%
Other values (62)1214035
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2596224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L214993
 
8.3%
S206476
 
8.0%
e155206
 
6.0%
i135234
 
5.2%
E127024
 
4.9%
124233
 
4.8%
T120883
 
4.7%
a108838
 
4.2%
r97787
 
3.8%
X91515
 
3.5%
Other values (62)1214035
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2596224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L214993
 
8.3%
S206476
 
8.0%
e155206
 
6.0%
i135234
 
5.2%
E127024
 
4.9%
124233
 
4.8%
T120883
 
4.7%
a108838
 
4.2%
r97787
 
3.8%
X91515
 
3.5%
Other values (62)1214035
46.8%

body
Text

Missing 

Distinct87
Distinct (%)< 0.1%
Missing13195
Missing (%)2.4%
Memory size28.6 MiB
2025-09-29T21:00:23.976526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length5
Mean length5.2792729
Min length3

Characters and Unicode

Total characters2880593
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSUV
2nd rowSUV
3rd rowSedan
4th rowSedan
5th rowSedan
ValueCountFrequency (%)
sedan248760
42.1%
suv143844
24.3%
cab33137
 
5.6%
hatchback26237
 
4.4%
minivan25529
 
4.3%
coupe19983
 
3.4%
crew16394
 
2.8%
wagon16180
 
2.7%
convertible10933
 
1.9%
g9333
 
1.6%
Other values (33)40608
 
6.9%
2025-09-29T21:00:24.368274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a398031
13.8%
e352374
12.2%
n338855
11.8%
S338282
11.7%
d262219
 
9.1%
V124796
 
4.3%
U119292
 
4.1%
C78559
 
2.7%
b77463
 
2.7%
s73869
 
2.6%
Other values (38)716853
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2880593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a398031
13.8%
e352374
12.2%
n338855
11.8%
S338282
11.7%
d262219
 
9.1%
V124796
 
4.3%
U119292
 
4.1%
C78559
 
2.7%
b77463
 
2.7%
s73869
 
2.6%
Other values (38)716853
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2880593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a398031
13.8%
e352374
12.2%
n338855
11.8%
S338282
11.7%
d262219
 
9.1%
V124796
 
4.3%
U119292
 
4.1%
C78559
 
2.7%
b77463
 
2.7%
s73869
 
2.6%
Other values (38)716853
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2880593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a398031
13.8%
e352374
12.2%
n338855
11.8%
S338282
11.7%
d262219
 
9.1%
V124796
 
4.3%
U119292
 
4.1%
C78559
 
2.7%
b77463
 
2.7%
s73869
 
2.6%
Other values (38)716853
24.9%

transmission
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing65321
Missing (%)11.7%
Memory size30.7 MiB
automatic
475677 
manual
 
17539
horse-driven
 
274
sedan
 
15
Sedan
 
11

Length

Max length12
Median length9
Mean length8.8948383
Min length5

Characters and Unicode

Total characters4389745
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic475677
85.1%
manual17539
 
3.1%
horse-driven274
 
< 0.1%
sedan15
 
< 0.1%
Sedan11
 
< 0.1%
(Missing)65321
 
11.7%

Length

2025-09-29T21:00:24.493624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-29T21:00:24.670307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
automatic475677
96.4%
manual17539
 
3.6%
horse-driven274
 
0.1%
sedan26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a986458
22.5%
t951354
21.7%
u493216
11.2%
m493216
11.2%
o475951
10.8%
i475951
10.8%
c475677
10.8%
n17839
 
0.4%
l17539
 
0.4%
e574
 
< 0.1%
Other values (7)1970
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4389745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a986458
22.5%
t951354
21.7%
u493216
11.2%
m493216
11.2%
o475951
10.8%
i475951
10.8%
c475677
10.8%
n17839
 
0.4%
l17539
 
0.4%
e574
 
< 0.1%
Other values (7)1970
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4389745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a986458
22.5%
t951354
21.7%
u493216
11.2%
m493216
11.2%
o475951
10.8%
i475951
10.8%
c475677
10.8%
n17839
 
0.4%
l17539
 
0.4%
e574
 
< 0.1%
Other values (7)1970
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4389745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a986458
22.5%
t951354
21.7%
u493216
11.2%
m493216
11.2%
o475951
10.8%
i475951
10.8%
c475677
10.8%
n17839
 
0.4%
l17539
 
0.4%
e574
 
< 0.1%
Other values (7)1970
 
< 0.1%

vin
Text

Distinct550297
Distinct (%)98.5%
Missing4
Missing (%)< 0.1%
Memory size35.2 MiB
2025-09-29T21:00:25.481639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.999585
Min length3

Characters and Unicode

Total characters9499929
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique541970 ?
Unique (%)97.0%

Sample

1st row5xyktca69fg566472
2nd row5xyktca69fg561319
3rd rowwba3c1c51ek116351
4th rowyv1612tb4f1310987
5th rowwba6b2c57ed129731
ValueCountFrequency (%)
automatic22
 
< 0.1%
wbanv13588cz578275
 
< 0.1%
1ftfw1cv5afb300534
 
< 0.1%
5uxfe43579l2749324
 
< 0.1%
5n1ar1nn2bc6328694
 
< 0.1%
wddgf56x78f0099404
 
< 0.1%
trusc28n2410220034
 
< 0.1%
wp0ca2988xu6296224
 
< 0.1%
wbxpa93416wd252823
 
< 0.1%
yv1mc67288j0528973
 
< 0.1%
Other values (550287)558776
> 99.9%
2025-09-29T21:00:26.555333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1919854
 
9.7%
2636863
 
6.7%
3612467
 
6.4%
5595556
 
6.3%
4574940
 
6.1%
0498895
 
5.3%
6487519
 
5.1%
7458863
 
4.8%
8455044
 
4.8%
c381530
 
4.0%
Other values (26)3878398
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9499929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1919854
 
9.7%
2636863
 
6.7%
3612467
 
6.4%
5595556
 
6.3%
4574940
 
6.1%
0498895
 
5.3%
6487519
 
5.1%
7458863
 
4.8%
8455044
 
4.8%
c381530
 
4.0%
Other values (26)3878398
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9499929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1919854
 
9.7%
2636863
 
6.7%
3612467
 
6.4%
5595556
 
6.3%
4574940
 
6.1%
0498895
 
5.3%
6487519
 
5.1%
7458863
 
4.8%
8455044
 
4.8%
c381530
 
4.0%
Other values (26)3878398
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9499929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1919854
 
9.7%
2636863
 
6.7%
3612467
 
6.4%
5595556
 
6.3%
4574940
 
6.1%
0498895
 
5.3%
6487519
 
5.1%
7458863
 
4.8%
8455044
 
4.8%
c381530
 
4.0%
Other values (26)3878398
40.8%

state
Text

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 MiB
2025-09-29T21:00:26.787699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length2
Mean length2.0006979
Min length2

Characters and Unicode

Total characters1118064
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowca
2nd rowca
3rd rowca
4th rowca
5th rowca
ValueCountFrequency (%)
fl82945
14.8%
ca73148
13.1%
pa53907
 
9.6%
tx45913
 
8.2%
ga34750
 
6.2%
nj27784
 
5.0%
il23486
 
4.2%
nc21845
 
3.9%
oh21575
 
3.9%
tn20895
 
3.7%
Other values (54)152589
27.3%
2025-09-29T21:00:27.218924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a199889
17.9%
n110349
9.9%
l108648
9.7%
c108264
9.7%
f82971
 
7.4%
t68644
 
6.1%
m60888
 
5.4%
p56632
 
5.1%
i54410
 
4.9%
o50032
 
4.5%
Other values (26)217337
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1118064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a199889
17.9%
n110349
9.9%
l108648
9.7%
c108264
9.7%
f82971
 
7.4%
t68644
 
6.1%
m60888
 
5.4%
p56632
 
5.1%
i54410
 
4.9%
o50032
 
4.5%
Other values (26)217337
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1118064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a199889
17.9%
n110349
9.9%
l108648
9.7%
c108264
9.7%
f82971
 
7.4%
t68644
 
6.1%
m60888
 
5.4%
p56632
 
5.1%
i54410
 
4.9%
o50032
 
4.5%
Other values (26)217337
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1118064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a199889
17.9%
n110349
9.9%
l108648
9.7%
c108264
9.7%
f82971
 
7.4%
t68644
 
6.1%
m60888
 
5.4%
p56632
 
5.1%
i54410
 
4.9%
o50032
 
4.5%
Other values (26)217337
19.4%

condition
Real number (ℝ)

Missing 

Distinct49
Distinct (%)< 0.1%
Missing11820
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean30.680052
Minimum0
Maximum982
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-09-29T21:00:27.422393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q123
median35
Q342
95-th percentile47
Maximum982
Range982
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.585974
Coefficient of variation (CV)0.4428276
Kurtosis99.271038
Mean30.680052
Median Absolute Deviation (MAD)8
Skewness0.77213291
Sum16782510
Variance184.57868
MonotonicityNot monotonic
2025-09-29T21:00:27.678744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1942280
 
7.6%
3526749
 
4.8%
3725938
 
4.6%
4425514
 
4.6%
4324937
 
4.5%
4224328
 
4.4%
3623144
 
4.1%
4123073
 
4.1%
220788
 
3.7%
419922
 
3.6%
Other values (39)290344
52.0%
ValueCountFrequency (%)
01
 
< 0.1%
17363
 
1.3%
220788
3.7%
310802
1.9%
419922
3.6%
511222
2.0%
1187
 
< 0.1%
1295
 
< 0.1%
1382
 
< 0.1%
14134
 
< 0.1%
ValueCountFrequency (%)
9821
 
< 0.1%
8971
 
< 0.1%
8491
 
< 0.1%
3132
 
< 0.1%
2892
 
< 0.1%
2801
 
< 0.1%
1931
 
< 0.1%
4913099
2.3%
4812712
2.3%
4711362
2.0%

odometer
Real number (ℝ)

High correlation 

Distinct172276
Distinct (%)30.8%
Missing94
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean68311.538
Minimum0
Maximum999999
Zeros152
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-09-29T21:00:27.950187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10491.1
Q128359
median52247
Q399108.5
95-th percentile170056.9
Maximum999999
Range999999
Interquartile range (IQR)70749.5

Descriptive statistics

Standard deviation53405.247
Coefficient of variation (CV)0.78178956
Kurtosis13.542316
Mean68311.538
Median Absolute Deviation (MAD)30480
Skewness1.8426065
Sum3.8168594 × 1010
Variance2.8521204 × 109
MonotonicityNot monotonic
2025-09-29T21:00:28.215322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11318
 
0.2%
0152
 
< 0.1%
99999972
 
< 0.1%
1029
 
< 0.1%
2158721
 
< 0.1%
218
 
< 0.1%
2913718
 
< 0.1%
818
 
< 0.1%
2131018
 
< 0.1%
3600717
 
< 0.1%
Other values (172266)557062
99.7%
(Missing)94
 
< 0.1%
ValueCountFrequency (%)
0152
 
< 0.1%
11318
0.2%
218
 
< 0.1%
39
 
< 0.1%
49
 
< 0.1%
517
 
< 0.1%
613
 
< 0.1%
713
 
< 0.1%
818
 
< 0.1%
911
 
< 0.1%
ValueCountFrequency (%)
99999972
< 0.1%
9801131
 
< 0.1%
9592761
 
< 0.1%
6949782
 
< 0.1%
6213881
 
< 0.1%
5809561
 
< 0.1%
5373341
 
< 0.1%
5222121
 
< 0.1%
5002271
 
< 0.1%
4957571
 
< 0.1%

color
Categorical

High correlation 

Distinct46
Distinct (%)< 0.1%
Missing749
Missing (%)0.1%
Memory size29.1 MiB
black
110970 
white
106673 
silver
83389 
gray
82857 
blue
51139 
Other values (41)
123060 

Length

Max length9
Median length8
Mean length4.6275032
Min length1

Characters and Unicode

Total characters2582554
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowwhite
2nd rowwhite
3rd rowgray
4th rowwhite
5th rowgray

Common Values

ValueCountFrequency (%)
black110970
19.9%
white106673
19.1%
silver83389
14.9%
gray82857
14.8%
blue51139
9.2%
red43569
 
7.8%
24685
 
4.4%
green11382
 
2.0%
gold11342
 
2.0%
beige9222
 
1.7%
Other values (36)22860
 
4.1%

Length

2025-09-29T21:00:28.445225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black110970
19.9%
white106673
19.1%
silver83389
14.9%
gray82857
14.8%
blue51139
9.2%
red43569
 
7.8%
24685
 
4.4%
green11382
 
2.0%
gold11342
 
2.0%
beige9222
 
1.7%
Other values (36)22860
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e332602
12.9%
l261465
 
10.1%
r241240
 
9.3%
i201026
 
7.8%
a196863
 
7.6%
b187020
 
7.2%
g125853
 
4.9%
w116124
 
4.5%
c111928
 
4.3%
k111012
 
4.3%
Other values (25)697421
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2582554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e332602
12.9%
l261465
 
10.1%
r241240
 
9.3%
i201026
 
7.8%
a196863
 
7.6%
b187020
 
7.2%
g125853
 
4.9%
w116124
 
4.5%
c111928
 
4.3%
k111012
 
4.3%
Other values (25)697421
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2582554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e332602
12.9%
l261465
 
10.1%
r241240
 
9.3%
i201026
 
7.8%
a196863
 
7.6%
b187020
 
7.2%
g125853
 
4.9%
w116124
 
4.5%
c111928
 
4.3%
k111012
 
4.3%
Other values (25)697421
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2582554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e332602
12.9%
l261465
 
10.1%
r241240
 
9.3%
i201026
 
7.8%
a196863
 
7.6%
b187020
 
7.2%
g125853
 
4.9%
w116124
 
4.5%
c111928
 
4.3%
k111012
 
4.3%
Other values (25)697421
27.0%

interior
Categorical

Imbalance 

Distinct17
Distinct (%)< 0.1%
Missing749
Missing (%)0.1%
Memory size28.8 MiB
black
244329 
gray
178581 
beige
59758 
tan
44093 
 
17077
Other values (12)
 
14250

Length

Max length9
Median length5
Mean length4.399437
Min length1

Characters and Unicode

Total characters2455273
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblack
2nd rowbeige
3rd rowblack
4th rowblack
5th rowblack

Common Values

ValueCountFrequency (%)
black244329
43.7%
gray178581
32.0%
beige59758
 
10.7%
tan44093
 
7.9%
17077
 
3.1%
brown8640
 
1.5%
red1363
 
0.2%
blue1143
 
0.2%
silver1104
 
0.2%
off-white480
 
0.1%
Other values (7)1520
 
0.3%
(Missing)749
 
0.1%

Length

2025-09-29T21:00:28.689478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black244329
43.8%
gray178581
32.0%
beige59758
 
10.7%
tan44093
 
7.9%
17077
 
3.1%
brown8640
 
1.5%
red1363
 
0.2%
blue1143
 
0.2%
silver1104
 
0.2%
off-white480
 
0.1%
Other values (7)1520
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a467148
19.0%
b314061
12.8%
l247279
10.1%
c244329
10.0%
k244329
10.0%
g239244
9.7%
r190608
7.8%
y178792
 
7.3%
e124856
 
5.1%
i61598
 
2.5%
Other values (13)143029
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2455273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a467148
19.0%
b314061
12.8%
l247279
10.1%
c244329
10.0%
k244329
10.0%
g239244
9.7%
r190608
7.8%
y178792
 
7.3%
e124856
 
5.1%
i61598
 
2.5%
Other values (13)143029
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2455273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a467148
19.0%
b314061
12.8%
l247279
10.1%
c244329
10.0%
k244329
10.0%
g239244
9.7%
r190608
7.8%
y178792
 
7.3%
e124856
 
5.1%
i61598
 
2.5%
Other values (13)143029
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2455273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a467148
19.0%
b314061
12.8%
l247279
10.1%
c244329
10.0%
k244329
10.0%
g239244
9.7%
r190608
7.8%
y178792
 
7.3%
e124856
 
5.1%
i61598
 
2.5%
Other values (13)143029
 
5.8%

seller
Text

Distinct14264
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size38.4 MiB
2025-09-29T21:00:29.197801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length42
Mean length22.990081
Min length3

Characters and Unicode

Total characters12847708
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4949 ?
Unique (%)0.9%

Sample

1st rowkia motors america inc
2nd rowkia motors america inc
3rd rowfinancial services remarketing (lease)
4th rowvolvo na rep/world omni
5th rowfinancial services remarketing (lease)
ValueCountFrequency (%)
inc86909
 
4.6%
services48240
 
2.6%
corporation47850
 
2.5%
auto47452
 
2.5%
credit46959
 
2.5%
motor45807
 
2.4%
llc45554
 
2.4%
financial44150
 
2.3%
ford36212
 
1.9%
remarketing35475
 
1.9%
Other values (8581)1395305
74.2%
2025-09-29T21:00:30.073650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1339787
 
10.4%
e1146331
 
8.9%
a1052355
 
8.2%
r962265
 
7.5%
n953446
 
7.4%
i917296
 
7.1%
o863200
 
6.7%
t796230
 
6.2%
c736349
 
5.7%
s671368
 
5.2%
Other values (37)3409081
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)12847708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1339787
 
10.4%
e1146331
 
8.9%
a1052355
 
8.2%
r962265
 
7.5%
n953446
 
7.4%
i917296
 
7.1%
o863200
 
6.7%
t796230
 
6.2%
c736349
 
5.7%
s671368
 
5.2%
Other values (37)3409081
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12847708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1339787
 
10.4%
e1146331
 
8.9%
a1052355
 
8.2%
r962265
 
7.5%
n953446
 
7.4%
i917296
 
7.1%
o863200
 
6.7%
t796230
 
6.2%
c736349
 
5.7%
s671368
 
5.2%
Other values (37)3409081
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12847708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1339787
 
10.4%
e1146331
 
8.9%
a1052355
 
8.2%
r962265
 
7.5%
n953446
 
7.4%
i917296
 
7.1%
o863200
 
6.7%
t796230
 
6.2%
c736349
 
5.7%
s671368
 
5.2%
Other values (37)3409081
26.5%

mmr
Real number (ℝ)

High correlation 

Distinct1101
Distinct (%)0.2%
Missing123
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13770.378
Minimum25
Maximum182000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-09-29T21:00:30.289342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile1800
Q17100
median12250
Q318300
95-th percentile30600
Maximum182000
Range181975
Interquartile range (IQR)11200

Descriptive statistics

Standard deviation9680.2193
Coefficient of variation (CV)0.70297414
Kurtosis11.443099
Mean13770.378
Median Absolute Deviation (MAD)5575
Skewness1.997564
Sum7.6937027 × 109
Variance93706645
MonotonicityNot monotonic
2025-09-29T21:00:30.559777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125001760
 
0.3%
116001751
 
0.3%
116501746
 
0.3%
121501722
 
0.3%
118501716
 
0.3%
113001716
 
0.3%
117501709
 
0.3%
123501702
 
0.3%
127001701
 
0.3%
119501694
 
0.3%
Other values (1091)541497
96.9%
ValueCountFrequency (%)
2530
 
< 0.1%
5044
< 0.1%
7523
 
< 0.1%
10033
 
< 0.1%
12540
< 0.1%
15045
< 0.1%
17569
< 0.1%
20054
< 0.1%
22560
< 0.1%
25083
< 0.1%
ValueCountFrequency (%)
1820001
 
< 0.1%
1780001
 
< 0.1%
1760001
 
< 0.1%
1720001
 
< 0.1%
1700003
< 0.1%
1660003
< 0.1%
1640001
 
< 0.1%
1630001
 
< 0.1%
1620001
 
< 0.1%
1610001
 
< 0.1%

sellingprice
Real number (ℝ)

High correlation 

Distinct1887
Distinct (%)0.3%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13611.359
Minimum1
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-09-29T21:00:30.837855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500
Q16900
median12100
Q318200
95-th percentile30600
Maximum230000
Range229999
Interquartile range (IQR)11300

Descriptive statistics

Standard deviation9749.5016
Coefficient of variation (CV)0.71627688
Kurtosis11.114646
Mean13611.359
Median Absolute Deviation (MAD)5650
Skewness1.9534444
Sum7.6063676 × 109
Variance95052782
MonotonicityNot monotonic
2025-09-29T21:00:31.051291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110004453
 
0.8%
120004450
 
0.8%
130004334
 
0.8%
100004029
 
0.7%
140003899
 
0.7%
115003876
 
0.7%
125003714
 
0.7%
90003689
 
0.7%
105003540
 
0.6%
150003386
 
0.6%
Other values (1877)519455
93.0%
ValueCountFrequency (%)
14
 
< 0.1%
10019
 
< 0.1%
1251
 
< 0.1%
15021
 
< 0.1%
17510
 
< 0.1%
200196
 
< 0.1%
225105
 
< 0.1%
250281
 
0.1%
275124
 
< 0.1%
3001282
0.2%
ValueCountFrequency (%)
2300001
< 0.1%
1830001
< 0.1%
1730001
< 0.1%
1715001
< 0.1%
1695001
< 0.1%
1690001
< 0.1%
1670001
< 0.1%
1650002
< 0.1%
1630002
< 0.1%
1610001
< 0.1%
Distinct3766
Distinct (%)0.7%
Missing12
Missing (%)< 0.1%
Memory size46.9 MiB
2025-09-29T21:00:31.577821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length39
Mean length38.998409
Min length4

Characters and Unicode

Total characters21793286
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604 ?
Unique (%)0.1%

Sample

1st rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
2nd rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
3rd rowThu Jan 15 2015 04:30:00 GMT-0800 (PST)
4th rowThu Jan 29 2015 04:30:00 GMT-0800 (PST)
5th rowThu Dec 18 2014 12:30:00 GMT-0800 (PST)
ValueCountFrequency (%)
2015505072
 
12.9%
pst395489
 
10.1%
gmt-0800395489
 
10.1%
wed166069
 
4.2%
tue163950
 
4.2%
pdt163310
 
4.2%
gmt-0700163310
 
4.2%
feb163053
 
4.2%
thu153750
 
3.9%
jan140815
 
3.6%
Other values (334)1501312
38.4%
2025-09-29T21:00:32.268828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
04819691
22.1%
3352794
15.4%
T1435298
 
6.6%
:1117598
 
5.1%
11061408
 
4.9%
2962306
 
4.4%
M673292
 
3.1%
5660463
 
3.0%
)558799
 
2.6%
G558799
 
2.6%
Other values (30)6592838
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)21793286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04819691
22.1%
3352794
15.4%
T1435298
 
6.6%
:1117598
 
5.1%
11061408
 
4.9%
2962306
 
4.4%
M673292
 
3.1%
5660463
 
3.0%
)558799
 
2.6%
G558799
 
2.6%
Other values (30)6592838
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21793286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04819691
22.1%
3352794
15.4%
T1435298
 
6.6%
:1117598
 
5.1%
11061408
 
4.9%
2962306
 
4.4%
M673292
 
3.1%
5660463
 
3.0%
)558799
 
2.6%
G558799
 
2.6%
Other values (30)6592838
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21793286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04819691
22.1%
3352794
15.4%
T1435298
 
6.6%
:1117598
 
5.1%
11061408
 
4.9%
2962306
 
4.4%
M673292
 
3.1%
5660463
 
3.0%
)558799
 
2.6%
G558799
 
2.6%
Other values (30)6592838
30.3%

Interactions

2025-09-29T21:00:14.139489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:08.266373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:09.654279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:11.074855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:12.458057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:14.500260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:08.526022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:09.919297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:11.326813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:12.755327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:14.858585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:08.797213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:10.312033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:11.639677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:12.982962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:15.204044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:09.070484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:10.537433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:11.926280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:13.277223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:15.540880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:09.351188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:10.822067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:12.198445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-29T21:00:13.709829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-29T21:00:32.382832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
colorconditioninteriormmrodometersellingpricetransmissionyear
color1.0000.0000.1020.0560.0660.0480.7080.091
condition0.0001.0000.0080.427-0.4040.4800.0000.387
interior0.1020.0081.0000.0630.0910.0620.0560.109
mmr0.0560.4270.0631.000-0.7180.9790.0200.697
odometer0.066-0.4040.091-0.7181.000-0.7040.015-0.817
sellingprice0.0480.4800.0620.979-0.7041.0000.0080.679
transmission0.7080.0000.0560.0200.0150.0081.0000.055
year0.0910.3870.1090.697-0.8170.6790.0551.000

Missing values

2025-09-29T21:00:16.246130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-29T21:00:17.289554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-29T21:00:19.330178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellermmrsellingpricesaledate
02015KiaSorentoLXSUVautomatic5xyktca69fg566472ca5.016639.0whiteblackkia motors america inc20500.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
12015KiaSorentoLXSUVautomatic5xyktca69fg561319ca5.09393.0whitebeigekia motors america inc20800.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
22014BMW3 Series328i SULEVSedanautomaticwba3c1c51ek116351ca45.01331.0grayblackfinancial services remarketing (lease)31900.030000.0Thu Jan 15 2015 04:30:00 GMT-0800 (PST)
32015VolvoS60T5Sedanautomaticyv1612tb4f1310987ca41.014282.0whiteblackvolvo na rep/world omni27500.027750.0Thu Jan 29 2015 04:30:00 GMT-0800 (PST)
42014BMW6 Series Gran Coupe650iSedanautomaticwba6b2c57ed129731ca43.02641.0grayblackfinancial services remarketing (lease)66000.067000.0Thu Dec 18 2014 12:30:00 GMT-0800 (PST)
52015NissanAltima2.5 SSedanautomatic1n4al3ap1fn326013ca1.05554.0grayblackenterprise vehicle exchange / tra / rental / tulsa15350.010900.0Tue Dec 30 2014 12:00:00 GMT-0800 (PST)
62014BMWM5BaseSedanautomaticwbsfv9c51ed593089ca34.014943.0blackblackthe hertz corporation69000.065000.0Wed Dec 17 2014 12:30:00 GMT-0800 (PST)
72014ChevroletCruze1LTSedanautomatic1g1pc5sb2e7128460ca2.028617.0blackblackenterprise vehicle exchange / tra / rental / tulsa11900.09800.0Tue Dec 16 2014 13:00:00 GMT-0800 (PST)
82014AudiA42.0T Premium Plus quattroSedanautomaticwauffafl3en030343ca42.09557.0whiteblackaudi mission viejo32100.032250.0Thu Dec 18 2014 12:00:00 GMT-0800 (PST)
92014ChevroletCamaroLTConvertibleautomatic2g1fb3d37e9218789ca3.04809.0redblackd/m auto sales inc26300.017500.0Tue Jan 20 2015 04:00:00 GMT-0800 (PST)
yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellermmrsellingpricesaledate
5588272014JeepGrand CherokeeLaredoSUVautomatic1c4rjfag0ec466276pa42.025180.0grayblackhertz corporation/gdp26000.024500.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5588282012DodgeGrand CaravanAmerican Value PackageMinivanautomatic2c4rdgbg1cr349287ma37.097036.0silvergrayge fleet services for itself/servicer8300.07800.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5588292012HyundaiElantraLimitedSedanNaN5npdh4ae7ch106397pa4.066720.0graygraychampion mazda10250.010400.0Wed Jul 08 2015 07:30:00 GMT-0700 (PDT)
5588302012NissanSentra2.0 SRSedanNaN3n1ab6ap3cl622485tn26.035858.0whitegraynissan-infiniti lt9950.010400.0Wed Jul 08 2015 17:15:00 GMT-0700 (PDT)
5588312011BMW5 Series528iSedanautomaticwbafr1c53bc744672fl39.066403.0whitebrownlauderdale imports ltd bmw pembrok pines20300.022800.0Tue Jul 07 2015 06:15:00 GMT-0700 (PDT)
5588322015KiaK900LuxurySedanNaNknalw4d4xf6019304in45.018255.0silverblackavis corporation35300.033000.0Thu Jul 09 2015 07:00:00 GMT-0700 (PDT)
5588332012Ram2500Power WagonCrew Cabautomatic3c6td5et6cg112407wa5.054393.0whiteblacki -5 uhlmann rv30200.030800.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5588342012BMWX5xDrive35dSUVautomatic5uxzw0c58cl668465ca48.050561.0blackblackfinancial services remarketing (lease)29800.034000.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5588352015NissanAltima2.5 Ssedanautomatic1n4al3ap0fc216050ga38.016658.0whiteblackenterprise vehicle exchange / tra / rental / tulsa15100.011100.0Thu Jul 09 2015 06:45:00 GMT-0700 (PDT)
5588362014FordF-150XLTSuperCrewautomatic1ftfw1et2eke87277ca34.015008.0graygrayford motor credit company llc pd29600.026700.0Thu May 28 2015 05:30:00 GMT-0700 (PDT)